Distributed acoustic sensing (DAS) has been widely studied, and it has been applied in several areas of seismology. Passive seismic monitoring is one of the areas in which the application of DAS is considered. Common challenges of DAS in geophysics include its unique response, redundant but relatively low signal-to-noise ratio measurement, and high throughput of data from the acquisition system. Here, we introduce our recent attempts to address these challenges in the implementation of real-time DAS passive seismic monitoring. Survey design is a critical step in evaluating the capability and limitation of the sensor network, and it is the same in DAS. The survey design algorithm is updated considering DAS response, and we observe the unique nature of a DAS network compared to a geophone network. Considering real-time monitoring, the large volume of DAS data creates a bottleneck if we simply apply an existing real-time processing model. To accomplish real-time processing, we distributed computation between a well site and processing center. At the well site, the traditional signal enhancement, as well as deep learning-aided signature detection, are performed. The number of traces is reduced, while information is enhanced. Then, the processing result is transmitted to the processing center to complete event-location and magnitude computations. We review the technology and discuss remaining challenges in passive seismic monitoring while leveraging DAS-acquired data.

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